Table of Contents
I got tired of the standard Kaggle workflow. Open a notebook, import pandas, start typing .describe(), throw some models at the wall. It works, but you don’t learn much, and three months later you can’t remember why you chose XGBoost over Random Forest.
So I built a toolkit that forces a better process: research first, then model. Document everything. Produce educational content as a natural byproduct of doing the work.
The Problem with Tutorial-Driven Kaggle
Most Kaggle tutorials follow the same pattern:
- Load data
- Explore data (
.info(),.describe(), some histograms) - Clean data (fill NAs with median, drop columns)
- Throw at Random Forest / XGBoost / LightGBM
- Submit
This gets you a score. It doesn’t get you understanding. When you move to a new competition, you start from zero because you never learned why anything worked.
Research-First ML
The toolkit enforces a different flow. Before any modeling code runs, you produce a research document that covers:
- Domain knowledge — What is this problem actually about? (Not “predict column Y” but “what real-world process generates this data?”)
- Hypotheses — Given what we know about the domain, what features should matter and why?
- Prior art — What have top Kaggle solutions done? What worked and what didn’t?
Only after research is complete do you touch the data. EDA then becomes targeted — you’re confirming or refuting hypotheses, not randomly exploring.
The Toolkit Architecture
kaggle_ml_toolkit/
├── loader.py # Data loading
├── cleaner.py # Preprocessing
├── feature_engineer.py # Feature creation
├── feature_selector.py # Feature selection
├── eda_engine.py # Automated EDA
├── model_selector.py # Multi-model comparison
├── model_optimizer.py # Hyperparameter tuning
├── ensemble_builder.py # Stacking/blending
├── evaluator.py # Metrics and persistence
├── interpreter.py # SHAP, PDP
├── submission_generator.py
└── content_generator.py # Blog post generation
Each module handles one stage of the pipeline. You configure a competition via YAML, and the toolkit guides you through structured decision points at each phase.
Steering Files as Workflow
The secret sauce is a set of steering files that define the decision points for each phase:
- Research — Collect domain knowledge, form hypotheses
- EDA — Targeted exploration to confirm/deny hypotheses
- Feature Engineering — Create features motivated by research findings
- Model Selection — Compare candidates with proper CV
- Optimization — Tune the top 2-3 candidates
- Ensemble — Combine complementary models
- Submission — Generate and validate submission files
- Content Creation — Produce blog posts and writeups
At each phase, I’m presented with options and make strategic decisions. The toolkit handles implementation. This means I focus on what to try rather than how to code it.
What It Produces
Every competition I run through this toolkit generates:
- A research document with domain findings and hypotheses
- An experiment log tracking every model iteration, what worked, and what didn’t
- A submission file (obviously)
- A blog post explaining the full journey
The blog posts aren’t an afterthought — they’re a core output of the workflow. If you can’t explain why you made a decision, you probably made it for the wrong reason.
Results So Far
I’ve run 7 competitions through the toolkit:
| Competition | Best Score | Key Learning |
|---|---|---|
| Titanic | 0.773 accuracy | Simpler models generalize better on small datasets |
| House Prices | 0.127 RMSLE | Stacking complementary models (linear + tree) beats any single model |
| Spaceship Titanic | 0.802 accuracy | CV-LB gap shrinks with dataset size |
| Digit Recognizer | Submitted | Gateway to deep learning |
| NLP Disaster Tweets | Submitted | Text classification basics |
| Store Sales | Submitted | Time series forecasting |
| ConnectX | Submitted | Agent-based competition |
The most interesting finding across all of them: the relationship between CV score and leaderboard score depends heavily on dataset size. On Titanic (891 rows), a higher CV score often means worse LB performance because you’re overfitting. On Spaceship Titanic (8700 rows), CV and LB track almost perfectly (0.9% gap).
Open Source
The toolkit, all competition code, research documents, and experiment logs are on GitHub. It’s MIT licensed — use it, fork it, adapt the steering files to your own workflow.
Source code: Kaggle ML Toolkit on GitHub